# LiLO: Lightweight and low-bias LiDAR Odometry method based on spherical range image filtering

arXiv: https://arxiv.org/abs/2311.07291 · HTML: https://mlsanity.com/paper/2311.07291/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2023.

## Built on by (most cited first, 1 of 1)

- [LiCAR: pseudo-RGB LiDAR image for CAR segmentation](https://mlsanity.com/paper/2501.13960/) (2025)

## Abstract (arXiv)

In unstructured outdoor environments, robotics requires accurate and efficient odometry with low computational time. Existing low-bias LiDAR odometry methods are often computationally expensive. To address this problem, we present a lightweight LiDAR odometry method that converts unorganized point cloud data into a spherical range image (SRI) and filters out surface, edge, and ground features in the image plane. This substantially reduces computation time and the required features for odometry estimation in LOAM-based algorithms. Our odometry estimation method does not rely on global maps or loop closure algorithms, which further reduces computational costs. Experimental results generate a translation and rotation error of 0.86% and 0.0036deg/m on the KITTI dataset with an average runtime of 78ms. In addition, we tested the method with our data, obtaining an average closed-loop error of 0.8m and a runtime of 27ms over eight loops covering 3.5Km.